New Preoperative Nomogram Using the Centrality Index to Predict High Nuclear Grade Clear Cell Renal Carcinoma

Figures & data

Table 1 Clinicopathological Characteristics of Training and Validation Data Sets

	Training (%)	Validation (%)	p value^a
Gender:			0.72
M	108 (64.7)	55 (67.9)
F	59 (35.3)	26 (32.1)
Tumor grade:			0.69
High	51 (30.5)	22 (27.2)
Low	116 (69.5)	59 (72.8)
T stage:			0.29
T1	121 (72.4)	51 (63.0)
T2	20 (12.0)	17 (21.0)
T3	21 (12.6)	10 (12.3)
T4	5 (3.0)	3 (3.7)
N stage:			0.79
Nx/N0	158 (94.7)	78 (96.3)
N+	9 (5.3)	3 (3.7)
M stage:			0.99
Mx/M0	156 (93.4)	76 (93.8)
M1	11 (6.5)	5 (6.2)

Note: ^aChi-square test.

Figure 1 CI score. Distance y (blue line) between middle axial plane and plane of maximum tumor diameter is calculated by multiplying the number of sections by thickness of the section. Distance x (green line) is measured from the central 90-degree axial reference point to the tumor center. Distance c (red line) is calculated and divided by tumor radius to determine CI.

Table 2 Comparison of Clinical Parameters and Anatomical Score of Cases with Different WHO/ISUP Grade

Clinical Parameters	Low (n=116)	High (n=51)	p value^a
Age (years)	57(50,64)	60(53.5,67)	0.06
Gender (n,%)			0.71
Male	70(60.3%)	33(64.7%)
Female	46(39.7%)	18(35.3%)
Tumor location (n,%)			0.92
Left kidney	52(44.8%)	24(47.1%)
Right kidney	64(55.2%)	27(52.9%)
Tumor size(cm)	3.12±1.31	6.55±2.22	<0.001
Anatomical score
Centrality index	2.4±1.13	1.25±0.69	<0.001

Notes: ^aChi-square test, wilcoxon test or Student’s t-test.

Table 3 Risk Factors for the Differentiation of the High- from Low-Grade ccRCC in the Different Models

	Variables	Coefficient	OR (95% Confidence Interval)	p value	AIC
Model A	Intercept	1.35	2.33(1.33, 3.78)	0.013	231.11
	Centrality index	−1.18	1.29(0.29, 2.81)	<0.001
Model B	Intercept	−2.51	0.08(0.03,0.16)	<0.001	254.26
	Tumor size	0.38	1.46(1.27,1.69)	<0.001
Model C	Intercept	−1.06		0.31	164.96
	Centrality index	−0.86	1.35(0.49, 2.54)	<0.001
	Tumor size	0.14	1.02(0.19, 1.58)	<0.001

Abbreviations: OR, odds ratio; AIC, Akaike’s information.

Figure 2 ROC curve.

Figure 3 Decision curve analysis. (A) Training cohort. (B) Validation cohort.

Figure 4 Calibration plot of nomogram. (A) Training cohort. (B) Validation cohort.

Figure 5 Nomogram evaluating risks of a clear-cell renal cell carcinoma being high grade.